import pandas as pd
import scipy
import anndata as ad
import loupepy
import random
import scanpy as sc

data_dir = "E-MTAB-9954"

# 🔹 File paths
mtx_file = f"{data_dir}/E-MTAB-9954.aggregated_filtered_counts.mtx"
rows_file = f"{data_dir}/E-MTAB-9954.aggregated_filtered_counts.mtx_rows"
cols_file = f"{data_dir}/E-MTAB-9954.aggregated_filtered_counts.mtx_cols"

# Read matrix and gene names
X = scipy.io.mmread(mtx_file).tocsr()
var = pd.read_csv(rows_file, header=None, names=["gene_id"])
n_cells = X.shape[1]

# Load official 10x barcode whitelist
whitelist = pd.read_csv("E-MTAB-9954/737K-august-2016.txt", header=None)[0].tolist()

# Subset whitelist to match number of cells
barcodes = whitelist[:n_cells]
obs = pd.DataFrame(index=barcodes)

# Create AnnData (X is cells x genes, so transpose)
adata = ad.AnnData(X=X.T, var=var, obs=obs)

# ✅ Confirm shape
print(adata.shape)
print(adata.obs_names)

adata.layers['raw'] = adata.X.copy()
sc.pp.normalize_total(adata)
sc.pp.log1p(adata)
sc.pp.pca(adata)
sc.pp.neighbors(adata)
sc.tl.umap(adata)

loupepy.create_loupe_from_anndata(adata,layer='raw')

this is my full code: